SYSTEM AND METHOD FOR IDENTIFYING LEARNER ENGAGEMENT STATES

• US 20170039876A1
• Filed: 08/06/2015
• Published: 02/09/2017
• Est. Priority Date: 08/06/2015
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: ALYUZ CIVITCI, NESE, OKUR, EDA, ARSLAN ESME, ASLI, ASLAN, SINEM, OKTAY, ECE, METE, SINEM E., SHLAIN, VLADIMIR, ABRAMOVITCH, PINI, KUNIN, ALEX, PAPINI, ILAN, STANHILL, DAVID, ROND, EYAL
• Application Number:
  US14/820,297
• Publication Number:
  US 20170039876A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G09B 5/00 Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G09B 7/00 Electrically-operated teaching apparatus or devices working with questions and answers mechanically operated G09B3/00; computing arrangements G06F

Abstract

Embodiments herein relate to identifying a learning engagement state of a learner. A computing platform with one or more processors running modules may receive indications of interactions of a learner with an educational program as well as indications of physical responses of the learner collected substantially simultaneously as the learner interacts with the educational program. A current learning engagement state of the learner may be identified based at least in part on the received indications by using an artificial neural network associated that is calibrated to the learner. The artificial neural network may be trained and updated in part by human observation and learner self-reporting of the learner'"'"'s current learning engagement state.

Claims

1. An apparatus to provide a computer-aided educational program, comprising:

one or more processors;

a receive module, to be operated on the one or more processors, to receive indications of interactions of a learner with the educational program and to receive indications of physical responses of the learner collected substantially simultaneously as the learner interacts with the educational program;

a learning state identification module, to be operated on the one or more processors, to identify a current learning state of the learner based at least in part on the indications of interactions and indications of physical responses; and

an output module, to be operated on the one or more processors, to output the current learning state of the learner;

wherein the current learning state of the learner is used to tailor computerized provision of the education program.

2. The apparatus of claim 1, wherein the learning state identification module is further to:

provide, to an artificial neural network associated with the learner, the received indications of interactions and the received physical responses of the learner;

receive, from the artificial neural network, a proposed learning state of the learner and a confidence level of the proposed learning state based on the provided indications; and

when the confidence level is above a threshold value, identify the received proposed learning state of the learner as the current learning state of the learner.

3. The apparatus of claim 2, wherein the artificial neural network is on the same or a different apparatus.

4. The apparatus of claim 1, wherein the learning state identification module is further to:

provide, to an artificial neural network associated with the learner, the received indications of interactions and the received physical responses of the learner;

receive, from the artificial neural network, a proposed learning state of the learner and a confidence level of the proposed learning state based on the provided indications; and

when the confidence level is not above a threshold value;

send to a learning state observer a request for the current learning state of the learner,

receive, from the learning state observer, an indication of the current learning state of the learner, and

send an update request to the artificial neural network, the request including the received current learning state of the learner, the indications of interactions of the learner and the indications of physical responses of the learner.

5. The apparatus of claim 4, wherein the learning state observer is a selected one of the learner or a human observing the learner.

6. The apparatus of claim 1, wherein the receive module is to receive the indications of the physical responses of the learner from a human observing the learner or from a physical response capture device associated with the learner.

7. The apparatus of claim 1, wherein the current learning state of the learner is identified by the learner.

8. The apparatus of claim 1, wherein the current learning state of the learner is a behavioral state or an emotional state.

9. An apparatus to implement a neural network comprising:

one or more processors;

a neural-network management module, to be operated on the one or more processors, to manage the artificial neural network;

a receive module, to be operated on the one or more processors, to;

receive indications of interactions of a plurality of learners with an educational program,

receive indications of physical responses of each of the plurality of learners collected substantially simultaneously as the each of the plurality of learners interact with the educational program, and

receive indications of a current learning state of at least one of the plurality of learners associated with the received indications of physical responses and the received indications of interactions with the education device of the each of the plurality of learners;

a neural-network training module, to be operated on the one or more processors, to train the artificial neural network based upon the received indications;

a request receiver module, to be operated on the one or more processors, to receive a request for a current learning state of a selected learner, the request including an indication of interactions of a learner with the educational device and an indication of physical responses of the learner collected substantially simultaneously as the learner interacts with the educational program; and

an output module, to be operated on the one or more processors, to;

in response to the received request, determine a current learning state and a confidence level for the determined current learning state from the artificial neural network; and

output the determined current learning state and the confidence level of the current learning state.

10. The apparatus of claim 9, wherein the confidence level is a scalar or a vector.

11. A method for computerized assisted learning, comprising:

receiving, by a learning state engine operating on a computing system, indications of interactions of a learner with a computerized educational program presented through a learning device;

receiving, by the learning state engine, indications of physical responses of the learner collected substantially simultaneously as the learner is interacting with the educational program;

identifying, by the learning state engine, a current learning state of the learner, based at least in part on the indications of interactions and indications of physical responses; and

outputting, by the learning state engine, the current learning state of the learner;

wherein the current learning state of the learner is used to tailor computerized provision of the education program.

12. The method of claim 11, wherein identifying a current learning state of the learner includes:

providing, by the learning state engine, to an artificial neural network associated with the learner, the received indications of interactions and the received physical responses of the learner;

receiving, by the learning state engine, from the artificial neural network, a proposed learning state of the learner and a confidence level of the proposed learning state based on the provided indications; and

when the confidence level is above a threshold value, identifying, by the learning state engine, the received proposed learning state of the learner as the current learning state of the learner.

13. The method of claim 11, wherein identifying a current learning state of the learner includes:

providing, by the learning state engine, to an artificial neural network associated with the learner, the received indications of interactions and the received physical responses of the learner;

receiving, by the learning state engine, from the artificial neural network, a proposed learning state of the learner and a confidence level of the proposed learning state based on the provided indications; and

when the confidence level is not above a threshold value;

sending, by the learning state engine, to a learning state observer, a request for the current learning state of the learner,

receiving, by the learning state engine, from the learning state observer, an indication of the current learning state of the learner,

sending, by the learning state engine, an update request to the artificial neural network, the request including received indications of the current learning state of the learner, indications of interactions of the learner and the indications of physical responses of the learner, and

identifying, by the learning state engine, the received current learning state of the learner.

14. One or more computer-readable media comprising instructions that cause a computing device, in response to execution of the instructions by the computing device, to:

receive, by a learning state engine operating on a computing system, indications of interactions of a learner with a computerized educational program presented through a learning device;

receive, by the learning state engine, indications of physical responses of the learner collected substantially simultaneously as the learner is interacting with the educational program;

identify, by the learning state engine, a current learning state of the learner, based at least in part on the indications of interactions and indications of physical responses; and

output, by the learning state engine, the current learning state of the learner;

wherein the current learning state of the learner is used to tailor computerized provision of the education program.

15. The computer-readable media of claim 14, wherein identify a current learning state of the learner includes:

provide, by the learning state engine, to an artificial neural network associated with the learner, the received indications of interactions and the received physical responses of the learner;

receive, by the learning state engine, from the artificial neural network, a proposed learning state of the learner and a confidence level of the proposed learning state based on the provided indications; and

when the confidence level is above a threshold value, identify, by the learning state engine, the received proposed learning state of the learner as the current learning state of the learner.

16. The computer-readable media of claim 14, wherein identify a current learning state of the learner includes:

provide, by the learning state engine, to an artificial neural network associated with the learner, the received indications of interactions and the received physical responses of the learner;

receive, by the learning state engine, from the artificial neural network, a proposed learning state of the learner and a confidence level of the proposed learning state based on the provided indications; and

when the confidence level is not above a threshold value;

send, by the learning state engine, to a learning state observer, a request for the current learning state of the learner,

receive, by the learning state engine, from the learning state observer, an indication of the current learning state of the learner,

send, by the learning state engine, an update request to the artificial neural network, the request including received indications of the current learning state of the learner, indications of interactions of the learner and the indications of physical responses of the learner, and

identify, by the learning state engine, the received current learning state of the learner.

17. The computer-readable media of claim 16, wherein the learning state observer is the learner self-assessing the learner'"'"'s learning state or a human observing the learner and assessing the learner'"'"'s learning state.

18. The computer-readable media of claim 17, further comprising:

facilitate, by the learning engine, training the human observer; and

facilitate, by the learning engine, evaluating the human observer.

19. The computer-readable media of claim 16, further comprising:

calibrate, by the learning state engine, the artificial neural network associated with the learner, wherein to calibrate the artificial neural network associated with the learner includes;

receive, by the learning state engine, an indication of an interaction with an educational program, an indication of substantially simultaneously physical responses, and an indication of a substantially simultaneous learning state for at least one other learner; and

send, by the learning state engine, a request to update the artificial neural network, the request to include the received indications for the at least one other learner.

20. The computer-readable media of claim 14, wherein a current learning state is a behavioral state or an emotional state.